ArticleSensors (Basel, Switzerland)2017
Real-Valued 2D MUSIC Algorithm Based on Modified Forward/Backward Averaging Using an Arbitrary Centrosymmetric Polarization Sensitive Array.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Multi-Target Parameter Estimation of the FMCW-MIMO Radar Based on the Pseudo-Noise Resampling Method.Sensors (Basel, Switzerland) · 2022Article
- Meter Wave Polarization-Sensitive Array Radar for Height Measurement Based on MUSIC Algorithm.Sensors (Basel, Switzerland) · 2022Article
- Two-Dimensional DOA Estimation for Three-Parallel Nested Subarrays via Sparse Representation.Sensors (Basel, Switzerland) · 2018Article
- 3D Target Localization of Modified 3D MUSIC for a Triple-Channel K-Band Radar.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
4 authors.
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Abstract
Two-dimensional multiple signal classification (MUSIC) algorithm based on polarization sensitive array (PSA) has excellent performance. However, it suffers a high computational complexity due to a multitude of complex operations. In this paper, we propose a real-valued two-dimensional MUSIC algorithm based on conjugate centrosymmetric signal model, which is applicable to arbitrary centrosymmetric polarization sensitive array. The modified forward/backward averaging, which can be applied to the PSA, is presented. Hence, the eigen-decomposition analysis process and spectrum function computation are converted into real domain, prominently reducing the computational complexity. Then, the direction-of-arrival (DOA) estimation is decoupled from the polarization parameter estimation so that the four-dimensional spectral peak search process is avoided. The theoretical computational complexity is discussed and the Cramer-Rao bound (CRB) of DOA estimation is derived in this paper. The simulation results indicate that the proposed algorithm achieves superior accuracy in DOA estimation and has low computational complexity.
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